Coveo was honored to join the Execs in the Know community at CRS Denver. As we spent time with fellow customer service and support leaders, we noticed some common themes in conversations, as well as during our panel with Ron Runyon, VP of Service Delivery – Americas at F5 Networks, Kal Kuchimanchi, Director Global Strategy & Central Operations – Customer Support at Uber and James Gallagher, VP of Customer Care and Fraud at Nordstrom.

1. Getting a Handle on Data is a High Priority

From data gaps to management to integration, one of the biggest obstacles we heard over and over to implementing a service transformation is data. As customers expect their experiences throughout the customer life cycle – whether with an agent or on a self-service community – to become more tailored, ensuring that the data carries through each channel seamlessly is paramount. But many leaders are still struggling.

2. AI is Exciting, But We’re Not Sure of the Yield Yet

From chatbots to voice assistants, AI and machine learning are ushering in a new era of customer support, and we need to see ROI. We heard the message loud and clear: the time to implement a strategic AI-powered solution to solve critical issues around CSAT and NPS is now.

3. We Need to Make Friends With Our IT Counterparts

Across the board, we heard that customer support leadership needs a partner for the dance, and that partner is the CIO organization. The IT organization needs to become a strategic and innovative partner for initiatives throughout the customer lifecycle.

4. Do We Know Enough About Our Customers?

Research has shown that customers expect personalization as a standard of service, but we’re still struggling to align our initiatives to the buyer and customer journey. What do they need across channels? How can data drive their experience in real time? Support leaders want answers to these questions.

5. It’s Time to Talk About Change Management?

We’re in a whole new world. With new technologies like machine learning and new expectations of customers, it can often feel like the only constant in the customer support world is change. Having a solid change management strategy and process in place is important now more than ever.

6. Agents Need to be Better Powered and Enabled

As we learn more about our customers, those insights need to be in the hands of the person who needs it most: the agents. Customers expect agents to know their history across channels, and their agents can be left in the dark with a frustrated customer without that information.

7. Show Us the Money

Money and resources are a constant roadblock, even as more and more C-suite leaders understand the critical role that customer success and support play in meeting overall business objectives. Don’t get “shiny object syndrome” when evaluating new options to increase CSAT and NPS, but focus technology investments on strategic initiatives overall.

8. The Issue of Scalability is Becoming More and More Important

This is an area where a technology initiative can help, such as using machine learning to power the experience customers have on your self-service community. As the customer base grows and customers’ expectations rise, support leaders need to plan for scaling their business.

9. We Have Too Many Channels and Too Much Content to Not Invest in Integration

Self-help content. Product sites. Chatbots. Corporate sites. The list can go on and on of the separate multiple channels, but for our customers, it’s one continuous, unified digital journey. It’s time to start treating our content strategy and customer digital experience that way.

10. It’s an Exciting Time to be a Customer Service and Support Leader

Customer support and experience is a rapidly growing field with ramifications into every other area of the business. There are days when it’s hard, but there are days when we know how much our work makes a difference for the people who matter most to us: our customers and employees.

One of the highlights of our panel was Ron Runyon sharing his story of “adaptive transformation” with F5 Networks. As the organization “disrupts itself in every area,” Ron’s team has skillfully and agilely implemented truly transformative initiatives throughout their customer lifecycle, including their implementation of Coveo. You can hear more about their service transformation by watching this video.

One of the first steps to beginning your service transformation is a self-service journey map. You can get started with developing yours with our free how-to guide and checklist. Download it here.

Guest post written by: Audrey Parent, Marketing Specialist at Coveo

The post 10 Things We Learned at CRS Denver appeared first on Execs In The Know.

The following is a guest post written by Andrew Kortina, Co-Founder and Co-CEO of Fin Analytics

The ‘Operations Flywheel’ is a framework for driving continuous improvement of CX operations quality or efficiency KPI like CSAT, NPS, Cost per Resolution, etc. In this post, I’ll discuss how we developed this methodology, explain why your organization should consider adopting it, and outline the basic steps in the flywheel.

How We Developed the ‘Operations Flywheel’

Before building Fin Analytics, the team behind Fin built a direct to consumer, on-demand executive assistant service. I remember when we were scaling up the customer operations team behind this assistant service, we had a conversation discussing the guiding principles for our operations team.

One of the key principles for this team was: “always improving” — the idea was that we wanted to design feedback loops into the systems, knowledge sharing, training, and processes for this team such that if we suddenly lost the top leadership of this team or stopped making new investments in the team, all of the quality and efficiency KPIs would continue moving in the right direction.

The inspiration for this principle came from our background in building consumer web software, where, for example, you want individual usage and total usership of your service to grow in a compounding manner each month, regardless of whether or not your product engineers release any new features.

While customer operations differs from pure software in that your KPIs directly depend on your front lines agents showing up and delivering awesome service, our goal was to design reinforcing feedback loops into all of the meta-work and processes for feedback to ensure continuous improvement based on the status quo operations (without continuous new investment).

Why Your Organization Should Use a Formal Methodology for Continuous Improvement

Designing operational processes (like QA, training, performance management) in a way that they lead to constant improvement in your organizations KPIs does not mean that you should stop undertaking new initiatives. It does, however, entail that you’ll continue to provide better and better service for your customers even when the new initiatives you take on run behind schedule or don’t give the incremental lift you originally estimated.

It also entails that any additional lift you do see in your KPIs from new initiatives is ‘gravy’ on top of the continuous improvement you’ll start seeing as your baseline.

This helps de-risk the execution risk of large, expensive, and time consuming strategic initiatives and ensures a better and better experience for your customers over time.

The other big advantage of adopting a framework for continuous measurement and improvement in your operations organization is that it enables rapid iteration.

The agile methodology has been adopted across almost every other part of the modern company — from product development to infrastructure engineering to performance marketing to sales — and there is no reason operations should not ‘join the party’ by taking advantage of continuous and comprehensive process measurement.

By taking advantage of agile / rapid iteration, an operations organization can better respond (sooner) when big new initiatives don’t go exactly as expect, and adapt quickly to get back on track.

The ‘Operations Flywheel’ at a Glance

Let’s take a brief look at the operations flywheel in action:

It starts with defining your metrics for success. These are the KPIs you set at the beginning of the year or the quarter. The flywheel approach is equally suited to driving quality metrics (CSAT, NPS) and efficiency metrics (hitting SLAs, Cost per Resolution).

For any given KPI, you first segment data to understand the biggest opportunities for improvement. This helps ensure your team spends time only in high leverage areas.

Next, within your segment you identify outliers, perform root cause analysis on the outliers, and discretize the root causes for failure and abnormality. This sequence of steps gives you a list of specific causes you can then prioritize (again, stack ranking by either frequency or cost), which you can then track and systematically drive down to zero with targeted improvements to coaching, training, internal tools, or process.

It’s important to properly attribute responsibility for each root cause to a responsible team, and it may be that multiple teams contribute to a specific set of outliers (for example, some of your low CSAT scores may ultimately stem from misaligned product expectations, which might be addressed by your product team; internal tools failures, addressed by a different team; and out of date processes / SOPs, fixed by yet another team).

Once you have methodically addressed your biggest opportunity, you rinse and repeat, with the next largest segment blocking you from hitting your goals.

The process is very similar to what you would see in a performance marketing team doing ‘growth hacking’ or a client application team doing when improving the speed of a slow application.

Takeaways

The formal methodology for continuous improvement in CX operations is based on our own experience building and managing large teams across Phoenix and Manilla, along with all of the best practices we have seen employed by top customer support organizations across a wide variety of industries, ranging from e-commerce to delivery to marketplace businesses to healthcare to personal finance.

We believe that using continuous and comprehensive measurement to design reinforcing feedback loops into every aspect of an operations organization — improvements to process, tools, QA, training, coaching — is the biggest opportunity in operations of the next five years, and the technology for taking advantage of this opportunity exists today.

For a deeper view of the ‘Operations Flywheel’ in action, our white paper is available upon request by emailing founders@finxpc.com.

Andrew Kortina is the co-founder and co-CEO of Fin Analytics, a real-time, cloud-based, comprehensive measurement platform that helps CX teams streamline and modernize their operations. Prior to Fin, Kortina Co-Founded the payments company Venmo.

The post The Operations Flywheel: Why You Should Adopt a Formal Methodology for Continuous CX Improvement appeared first on Execs In The Know.

When people talk about automation, and how computers are taking over customer support and why we no longer need human agents, I think of my dad.

He hates anything that has to do with automated things and computers (Little Britain is likely to blame). He was born in a world where you would speak to toll collectors, supermarket cashiers, and bank managers. Now having to deal with machines instead of real people really gets to his nerve. 

And he’s not alone.

They say humans yearn for human contact and, if given the choice, they will always prefer interacting with another human. The logic behind this is that we are a highly social species, we live in society and are innately designed or programmed to interact with each other.

This argument has some scientific truth to it: in one study by Ryan W. Buell, customers who used ATMs more than human tellers had a lower level of satisfaction with their banks. Buell, who is now at Harvard Business School, found in a different experience that people calling Metlife to claim death-related insurances, receive an automated condolences message – and were a bit freaked out.

On the other hand, wasn’t this argument of human contact used against removing toll operators? Or even the iconic phone operator? Against self-service checkout at supermarkets?

The thing is, not all customers are like my dad. Personally, the less humans I need to ‘deal with’ in everyday ‘normal’ tasks, the better. If I can solve a problem or get something done without talking to a human I feel productive and thankful about it, actually.

In other words, this is what we all need to understand about automation in customer support. You’re automating it for the people. And it can’t be feast or famine. It needs to be just right. It needs to be nuanced.

Since I joined Unbabel as the VP of Global Alliances, I’ve been working closely with companies like Zendesk, Salesforce and Convergys, to figure out ways to improve customer experience and make it more efficient.

And when it comes to automation there’s one rule of thumb: Automation is great, if – and only if – it brings you closer to your customer. Anything else is a bad move.

So how do you know what to automate and what not to?

Well, this easy to follow checklist is the perfect way to get you started.

1. Understand Your Product

It all comes down to your business and product. Is your product easy to implement? Or do you usually have to walk your customers through implementation? Are you B2B or B2C? How’s your customer service team organized? Do you have physical stores or is everything online?

These are all relevant questions you should be asking yourself at this point. If you have a complex product you need to make sure that automation doesn’t stand in your way. You want it to be the source of simplicity and efficiency not of frustration and negativity.

A good example that I like to give is Bang & Olufsen, the Danish high-end consumer electronics company that designs and manufactures audio products, television sets, telephones, and so on. So basically their traditional customer base is people walking into the store and talking to the person they bought the product from. I mean, if you’re going to spend 10k-20k in a TV set you might as well talk to a human to make sure you’re making the best choice and that you have excellent customer service in the future.

However, more recently, Bang & Olufsen diversified and released a new line of products called B&O Play where they are selling headphones and speakers to a younger audience at a more affordable price. So, in this case, they had to flip their model. These new products are simple, easy to use, and highly scalable, and their target audience loves automation. In the end, to deal with scalability what they did was to keep providing excellent customer service by having human agents while using AI to make those agents much more efficient.

So, in the end, it’s all about the product you’re selling and who you’re selling to, which brings me to the next point.

2. Know Your Audience – Age And Geography

Is it crucial that before you look into automation you get to know your customer. How old are they? Where are they from? What kind of help do they need while using your product?

Most people agree on this: if millennials are your audience, automation is great. They’re not that much into talking to other humans. And they hate talking to anybody on the phone. In fact, Help Desk claims 72% of millennials literally do not want to talk to your customer service team.

But does this mean they want everything to be automated? Well, not necessarily. And here’s where most people get it wrong. It’s not that millennials don’t want to talk to you, they just want a better customer experience overall, and if that means less of hassle they’re gonna go for it.

Another thing you should bear in mind is looking at where your customers are coming from. Most people neglect the importance of culture in consumer behaviour, and yet it is paramount.

Do people in Japan like chatbots? Or do they prefer talking to a human? What about in France? Or in the US? According to a survey by LivePerson, for instance, European countries tend to be more receptive to chatbots. But people in the US? Not so much. 59% of Americans said they preferred talking to a human other than a machine.

Bottom line is: get to know your customers, and how old they are and where they are from to deliver the best tailored customer experience.

3. Analyse Your Common Queries

The truth is, your customers are used to a certain standard and they know what to expect. So make sure you look back and understand the kind of help they need from you. What type of questions do they ask? Are those easy to solve problems? Things you can automate or put in your FAQs?

Look into your common queries and if you have a lot of those there’s just so much you can do to make the whole process better. There’s a lot you can automate and improve. So why not put part of your customer service operations on autopilot?

That’s exactly what platforms such DigitalGenius are doing by using AI to understand conversations, automate repetitive processes and solve your customers’ problems. This will help you automatically deal with the most common queries such as refund requests, order status inquiries, cancellations, and so. And the great thing about this is that it will allow you to cut down costs and keep things efficient.

4. Find The Perfect Tools

Automation can be a slippery slope. When done wrong, it can actually backfire, duplicating customer queries and support operations, and annoying the customer in the process.

With the rise of artificial intelligence and big data, customer service has evolved into a field that’s all about efficiency, automation, and personalization. We’ve seen the emergence of new tools in self-service; live chat’s dominant performance in customer satisfaction; the widespread adoption of advanced analytics; and the automation of repetitive processes, which has allowed companies to cut down costs and keep their operations efficient.

But, at the end of the day, it’s not just a matter of implementing chatbots and automating replies. It’s much more than that.

In order to really improve the customer experience, boost operational efficiency, and reduce costs you need to find the right tools that integrate with your workflow. 

If you’re analysing which tools to use check who their customers are, understand if the solution they’re presenting will meet your needs, test it and see if it improves customer satisfaction and if your agents are happy with it. 

5. Automate What You Can

The post Why Automation in Customer Support Can Be A Slippery Slope appeared first on Execs In The Know.

The following is a guest post written by Jeff Saegner, VP of Customer Success, Helpshift

Customer support agents put up with a lot. You might have been in a situation where you felt the need to apologize to an agent for your mood — ‘I’m not annoyed with you, I’m frustrated with the situation.’ Most of the customer service agent’s job is to make us feel heard and valued as customers in moments when we’re not. But agents today don’t have a lot of time to spend empathizing with you, because they’re too busy gathering basic information about you and your problem. With little to go on, most agent interactions start with the most basic questions, whether that be asking a customer to verify their identity, provide an account number or (gulp!) ask a customer to repeat their issue after it’s been escalated.

As you know, Customer Satisfaction (CSAT) metrics are extremely sensitive to the quality of the agent interaction. When they perform well — solving issues with efficiency and empathy — CSAT goes up. When agents are overworked and unable to give the right amount of attention to each customer, CSAT goes down. When we talk about automation in customer service, we often discuss how this impacts the customer. Recent data from Helpshift shows that when bots and agents work together, CSAT is seven percent higher than when an agent or bot handles an issue solo.  While this is great news for the customer, a deeper dive shows that agents are benefiting as well, by having more time to spend resolving customer problems. Here’s a closer look at our data and the story it tells about agents and bots working together to help customers.

Shorter hold times and ticket deflection

Helpshift data shows that 62 percent of people dread waiting on hold, and will put off calling customer support just to avoid doing so. And it’s not just on the phone. Even live chat requires customers to wait with a chat session open until an agent is available to respond. A new way to offer support is through asynchronous messaging, which allows customers to start a conversation and come back to it without losing the session — just like how they message their friends on their favorite messaging apps. 

By pairing messaging with bots, response times are instant. Aside from replying instantly, bots do the rote work of gathering information for account access, identity verification, and issue classification before handing it to an agent. This warm handoff ensures that when an agent takes an issue, they have all the details they need to begin handling the customer’s issue immediately. And when bots are integrated into your back end systems to enable process automation, an agent might not even need to get involved at all. As a result, bot automation can deflect a big chunk of tickets from an agent’s queue, freeing up their time to focus on customer problems that really need their attention.

Sharing the workload

Our data shows quite dramatically how bots can handle an outsized portion of the support ticket workload. Over the last quarter, the number of overall messages sent by chatbots on the Helpshift platform increased 35 percent, while for agents there was only an increase of eight percent. The numbers are even more striking when looking at the average number of messages sent per ticket, with a nine percent increase for bots and a 13 percent decrease for agents. Bots are able to dramatically scale the number of tickets customer support teams can handle while simultaneously decreasing the number of messages agents need to send per ticket to resolve an issue — all while improving CSAT.

Support on the customer’s timeline

Automation empowers customers to seek help on their timeline, rather than planning their day around getting a problem resolved. Bots thrive in an asynchronous messaging environment where customers can begin a conversation and come back to it later. A bot won’t close out a session just because the person on the other end has stopped responding. And if a bot escalates an issue to an agent, the customer will receive a push notification so they can respond at a time that suits them best. All of this work happens entirely on the customer’s timeline — so they’re not left feeling like their time has been wasted. It’s no wonder then that CSAT is highest when agents and bots work together. 

Compared to other channels, when bots augment the work of agents within a messaging environment it leads to a better experience for customers and a more efficient work experience for agents. Bots deflect a large number of tickets, so agents can focus on the most important ones. With fewer tickets to get through and with no customers waiting on hold, agents can take more time to provide a detailed and complete response to the customer, or even put processes in motion for resolution (such as issuing a refund) without making the customer wait. By the time the customer does come back to the chat, the only thing left to say might be ‘Thanks!’

The post Customers Are Happier When Agents and Bots Work Together…But So Are Agents appeared first on Execs In The Know.

Although distinctly different, “manager” and “leader” are often used interchangeably.  But those responsible for managing the contact center coaching program also have to lead the engagement, performance and development of people! 

MIT defined coaching as a sophisticated management style that requires developing a relationship that empowers employees by building confidence and competence. 

Leadership is that intentional relationship between the leader and the led.  Respect, confidence, caring, and meeting needs are all required in order to build trust with team members so we can help them see their opportunities while ensuring they remain open and receptive to hearing feedback and discussing alternate approaches.
“Our contact center glosses over development for frontline leaders,” is something we commonly hear from senior executives.   But you can’t build a great contact center by allowing or tolerating mediocre leaders. Everyone responsible for Agent development must also be developed themselves! 

In one study, DDI reported that business outcomes of mediocre leaders in leadership roles can be devastating:

1. 69% admitted the promotion of the wrong leaders resulted in loss of employee engagement.

2. Nearly 2/3rds experienced loss of productivity.

3. 1 in 4 respondents indicated their business suffered a loss in profitability.

Let’s be clear, everyone responsible for the performance improvement of people is in a leader role.  But being a leader, and developing leadership competency, are two different things.  If we want to empower our people, develop them to their potential, and engage them to put forth effort and innovation, we cannot continue to enable and tolerate mediocre leadership skills.

Remember, the best mechanism for performance improvement is effective coaching.  And effective coaching requires a formula consisting of defined and communicated Procedural Infrastructure + interpersonal leadership skills.

And when you put together a proper system, and develop interpersonal leadership, then you can transform your contact center into one that delivers effective coaching within a culture of development, not just compliance!

A Better Way…

“There’s good news for contact-center leaders. Once they understand the issues that hamper coaching effectiveness, new digital tools can help address them by improving the availability, targeting, and delivering of coaching inputs.”

McKinsey & Company

How do you develop coaching competency and consistency, at scale, across your programs, teams, sites and at home environments?  You leverage technology to augment both your processes and your people!

The 5 Steps to Improving Coaching Effectiveness

There are 5 key People & Process Steps that will increase your Coaching Effectiveness:

1. Create and communicate your coaching system – expectations, procedures, and methods

2. Train coaches how to communicate and develop others – follow-through and leadership skills

3. Provide visibility into performance and coaching – frequencies, behaviors, and action plans

4. Measure coaching effectiveness – whether people improve or not after being coached

5. Coach your coaches – improve coaching competency and effectiveness

And while enterprise leaders certainly have to define and communicate the system of procedures, approaches, and expectations in order to create a consistent operating framework, there are now innovative technology tools that can carry out that system, and support the remaining steps.

That means you don’t have to try and solve each of the steps individually, rather you can apply a collective solution that systemically supports all the steps within the overall operation and its people.

As an example, we’re going to explore AmplifAI’s coaching effectiveness capabilities, and show you how we’ve been creating increased coaching effectiveness, resulting in rapid, turn-around engagement and performance for direct and BPO contact centers alike!

“Automate supervisors’ administrative tasks!  Many of the activities that keep supervisors and team leaders off the contact-center floor are prime candidates for automation”

McKinsey & Company

How does AmplifAI improve Coaching Effectiveness?

AmplifAI is a cloud-hosted, AI-powered, all-in-one Performance Acceleration Platform for Sales and Service.

And while AmplifAI’s solutions include data integration, performance analytics, gamification, learning, coaching, and quality, we’re going to focus here on the platform’s ability to drive more effective coaching!

AmplifAI has freed up managers from manual analysis, interpretation, and distribution of performance data, resulting in a return of up to 50% more-time spent coaching and developing people!

• With Agents having direct access to performance, coaching commitments and coaching history, they’re empowered to self-identify gaps and take independent action even before supervisors’ coach them – resulting in increased employee engagement and accountability!
• The embedded coaching form and workflow ensures coaches are consistently applying your methods, that you have consolidated documentation, and the ability to easily inspect process.
• The post-coaching workflow and progress-tracking ensures everyone has visibility into coaching commitments, history and results.
• The continuously updating Coaching Effectiveness Score and ranking keeps supervisors informed of the impacts of their developmental efforts.
• Coaching dashboards highlight for leaders which coaches need coaching skill development!

As a result, AmplifAI clients have seen exciting results, and have finally vanquished the age-old challenge of not getting a return on their coaching time investments:

• Improved Performance & Customer Experience
• Winning additional client headcount
• Retaining talented people
• Improving financial performance

Summary

The future of success in your contact center is contingent on how you impact performance in ways that are both immediate and sustainable. And it doesn’t matter whether your agents are on-site, at-home, full-time, part-time, or temporary – you must deliver on performance.

Coaching is one of the most significant tools we can use to deliver on the engagement and performance of our people – but we must develop our processes, our people and leadership skills, and our technology tools, in order to overthrow the pervasive challenges to achieving greater coaching effectiveness and supercharging contact center performance!

The post More Important Than Ever: Coaches Must Be Leaders appeared first on Execs In The Know.

As the business world ups its competitive game for a digital age, customer satisfaction can no longer be treated as merely nice to have, or as an ambition metric for the corporate scorecard. On the contrary, it is a business essential that will determine the success or failure of brand advocacy, customer loyalty and consumer adoption and as a result, competitive advantage.

Many organizations still find themselves in catch-up mode on their digital transformation strategies. But, regardless of where they are on this shift, it is critical that customer experience (CX) remains at the heart of change and innovation.

Redefining CX for the 21^st century is not and cannot be about technology alone. Building a strategy that combines right-fit technology with customer accessibility and human-led engagement is the only means to address the key challenges that companies face as they endeavor to keep up with higher customer expectations in today’s always-on consumer world.

Concentrix Solv™ has been designed to respond to exactly those needs by rethinking the traditional customer support journeys. It bypasses the challenges linked to the inflexibility of a premise-based customer management model, such as staffing and meeting digitally-driven customer demand.

Delivering Service and Satisfaction at Scale

Offering always-on service comes at a cost. Too often, the cost/quality conflict reaches a tipping point that defies the return on investment (ROI) and forces a transformation roll-back that ultimately risks degradation of CX, rather than enhancement.

Consider the demand curve over a 24/7 period as it tails after 8-10pm. Traditional operating models struggle to sustain any-time support across global time zones when customer demand reduces in volume, as the ROI to keep service teams available is severely tested. That challenge is exacerbated when it comes to multi-lingual support and more often than not, results in non-English speaking customers being limited to a choice of self service, English-only support or nothing at all.

Automation plays a key role in creating a more dynamic CX and when done well, self-service can be both efficient and effective at customer support. Executed poorly, or in isolation from existing customer support channels however, there is a risk that customers enter a cycle of digital failure, channel shunt and repeated effort. Net promoter score (NPS) subsequently declines, gross contact volumes increase and with them, so does cost.

Why Gig?

In our digital age, the workforce is increasingly mobile and work can often be done from anywhere. With job and location decoupled, there are new opportunities outside of the brick-and-mortar constraints of conventional operations.

By leveraging the autonomy of the gig economy, Solv unlocks access to a talent marketplace that matches knowledgeable advocates, or Solvers, to customer demand to offer support. For the customer this removes the barriers of office hours and offers them the flexibility for true anytime, anywhere support from all around the world at a time that is convenient to them.

From a brand perspective the benefit from the same supply/demand optimization is high. In fact, it increases the customer support that brands have been able to offer in the traditional model with lower-cost-to-serve and faster scalability.

And it is not just consumer behavior that has changed in a digitally driven world, but so too has the workforce of the future.

Companies such as Uber, Airbnb, and TaskRabbit have been leveraging the gig economy for more than a decade, providing qualified people with a means to earn extra money, using the resources they already have. For businesses, the gig economy is a brilliant way to bring on workers only when there is work to be done. And for workers, it provides a flexible, customizable way to bring home a paycheck without the burden of a typical 9-to-5 and all the hidden costs that come with that in commutes, city real estate premiums and long days and nights.

Concentrix Solv harnesses the gig economy for crowd-based customer support and takes CX to new possibilities.

Solvers are ubiquitous. They can solve from anywhere and at a time that is convenient to their lifestyle. Solvers are a crowd of independent contractors, made up of tech enthusiasts, gig economy superstars and expert customer service workers.

Solvers have the flexibility to work on their terms when and where they want to. Solv connects them directly to the brands they love, allowing them to get paid for their expertise, offering something new for the 21^st century employee seeking to rebalance work/life in a way that pre-millennial generations did not have as a cultural priority.

How Solv Works

Solv connects companies to a global talent pool of confident researchers, enthusiastic tech fans and customer advocates who have a passion for brands. Solvers are like-minded consumers and customers of the same products and services used by the customers who need help. They engage asynchronously via messaging to find answers to customer issues and, once solved, they get rewarded for their success. This on-demand model is reciprocated for the brands who choose to operate on Solv, meaning they only pay for satisfied customers. Leveraging positive gig economy functionality into customer service offers increased access, improved satisfaction in an efficient, results-driven pricing model.

Connecting the relationship back to technology is Solv’s smart use of data – Solv uses the high C-Sat responses to guide and teach AI and BOT strategies better, automating where it makes sense for the CX and improving process where it may not.

Raising the Customer Experience Bar

Business isn’t just digital these days—it’s competitively experience-driven. With the vast majority of customers noting that their experience with a brand is equally important to the products and services it offers, there’s no ignoring quality of service. New ways of doing business like Solv are creating exciting opportunities for companies to differentiate themselves in the marketplace. As the standards of customer experience remain high alongside the need to keep costs low, companies will need to keep pace with the technologies that will allow them to compete at scale.

Person-to-person customer care hasn’t gone anywhere—it’s just a human-led digital engagement now.

Learn more and see how Solv could be right for your business:

Contact paula.kennedy@concentrix.com or follow us at www.concentrix.com

The post How Will The Gig Economy Help Us Rethink Customer Support In The Digital Age? appeared first on Execs In The Know.

“The best-laid plans of mice and men often go awry.” – Robert Burns, “To a Mouse” 

CX leaders today know this all too well. In today’s business environment, I often hear that the only constant is change. Our customers are rapidly changing and shifting their demands for their support experience. With every new graduating class of the workforce, our employees are coming in with their own set of needs for their experience. The leadership team analyzes and recognizes new threats and brings in their own set of expectations.

How do you actually accomplish your strategic initiatives for transformation when everything is constantly shifting?

The answer: Adaptive Change.

What is Adaptive Change and Why Does It Matter

Ron Runyon, VP – Support Delivery at F5, described adaptive change as still being able to climb a mountain – even when the weather conditions are foggy and you can’t see the summit.

He shared their own story of adaptive change in the midst of a sweeping business transformation in the video below.

Adaptive change, or “change that requires new learning for problem definition and solution implementation” according to change theorist Ronald A. Heifetz, is critical for today’s support leaders as they face what can feel like insurmountable challenges in a rapidly changing business environment. As opposed to “technical change,” the responsibility for the solution resides with the followers, not the leader or expert. In one of the seminal papers on the subject, researchers identified six guiding principles for adaptive change.

1. “Get on the balcony.”

This is not the time to be in the weeds – it’s a time to get a view from above where you can identify patterns and resolve issues. Common reactions to change are work avoidance and power struggles, but if you’re right alongside the team, you won’t be able to spot them.

2. Identify the real challenge

Don’t shy away from examining your people, your structures and everything in between as your organization adjusts to the adaptive challenge. What is stopping your organization from adapting to changes quickly?

3. Keep tabs on stress

These sweeping changes generate stress and too much will debilitate your workforce. Regulate stress by reassuring employees and helping them identify their own issues with change.

4. Maintain disciplined attention

Be open to contrasting opinions and perspectives. Every change will bring out its own issues and it’s important to create an environment where issues can be openly discussed and resolved.

5. Empower your workers

Don’t let senior managers shoulder the responsibility for responding to swift market changes or customer expectations – the decision to act rests on all. Give every worker the responsibility and ability to make the decisions that they see fit. Don’t let bad habits of work avoidance in the face of change translate to responsibility avoidance.

6. Facilitate open communication

Recognize that in these situations, the dissenting opinions are often silenced or without a proper venue, brought up at the wrong times.

Other Theories of Change Management

I am a Prosci-certified change management professional. Their methodology follows a different approach, focusing heavily on the people side of change management. To drive change in your organization, you should follow ADKAR:

A – Awareness of the need to change. During this stage, your goal is to help your audience see the light of why the change is needed. Enlighten and inform them of the current state to motivate them when to embrace the change.

D – Desire to support the change. This is your time to create some hype for the changes you’ll be implementing. Think of this as your marketing campaign to your stakeholders and larger audience.

K – Knowledge of how to change. Even with the most exciting rollout of your changes and the motivation to change at full force, without the knowledge of how to change, your changes will fall flat.

A – Ability to demonstrate skills and behaviors. In practice, I can walk you through how to yodel. You can even memorize the instructions so well that you can repeat them back to me. But will you actually know how to yodel and be comfortable doing it in front of an audience? No. Hands-on facilitation is needed at this stage to avoid costly errors later on.

R – Reinforcement to make the changes stick. Change is an ongoing process, and your changes and strategy will evolve. This stage is where you ensure your changes still work for your organization and maintain consistency with your changes.

How do you handle change in the face of transformation?

Expect to hear answers to this question during my panel with leaders from Nordstrom, F5 Networks and Uber at Customer Response Summit, in Denver, on September 17. We’ll discuss ways and initiatives to improve service, personalization and insight using AI – definitely an adaptive change challenge for many organizations!

The post What Is “Adaptive Change” and Why It Matters for Support Leaders appeared first on Execs In The Know.

The following is a guest blog written by Joey Greenwald, Senior Director of Marketing, Directly.

Creating a truly effective AI-powered virtual assistant (VA) isn’t simple. You don’t just drop in a few lines of code and a data set and then let an algorithm do its thing. As we wrote recently, there are four main reasons AI projects fail and one of them is a lack of maintenance. It takes a dedicated and ongoing commitment to do it right. Just ask Amazon — the company has spent billions of dollars on research and development and reportedly has 10,000 employees dedicated to making Alexa more understanding, empathetic and ultimately effective.

Almost like a parent, organizations that successfully deploy AI-powered products are continuously teaching and nurturing them over time — even after they are mature, effective and by all standards “intelligent.” They raise them.

In our space, AI is being deployed as a tool to help companies automate portions of their customer service operations. The problem we at Directly are trying to solve is to help businesses efficiently resolve customer support issues. We do this with AI-powered virtual agents that can quickly identify the intent of the question, serve automatic content to resolve the issue, and escalate when needed to human support agents.

The key metric for support automation is “resolution rate.” Simply put, how many support cases can be completely resolved by AI-powered virtual agent without escalating those issues to an agent?

When we discuss resolution rate with our clients, we look at the curve shown below and segment potential customer support questions into three sections: The head (red) represents the most common questions, while the middle tail (green), and the long tail (teal) are the least commonly asked and most complex questions.

The area of this curve where an AI-powered virtual agent is able to successfully answer questions depends largely upon how companies (and their vendors) support their AI initiative over time.

In this post, I’ll explain the steps involved in raising an AI-powered virtual agent. Most importantly, I’ll show how to increase its level of maturity and its success rate. I’ll also share some rough benchmarks for support resolution rates with each stage of AI maturity, as illustrated by our first contact resolution curve.

Childhood: Creating and launching your virtual support agent

Let’s say your company has committed to building and launching a virtual support agent. Here’s how you’d first bring it to life.

Step 1: Choose a VA vendor

The first step is to choose a vendor from which to license your virtual assistant. This decision typically depends upon the level of features you need, though it may also be influenced by any pre-existing vendor relationships (e.g. if you use Azure or other Microsoft technology, that might steer you to Microsoft Bot Framework). Many companies start with broadly applicable services such as these: Amazon Lex, Microsoft Bot Framework, Zendesk Smooch, Salesforce’s Einstein, Meya, and Google Dialogflow. For more robust (and expensive) solutions, companies look to Nuance and [24]7.ai.

Step 2: Cluster historical data

Next, you’ll need to gather all relevant customer support data representing your history of customer questions and interactions. Your AI will need this baseline structured data, likely in the form of a .CSV file, as you start building its intelligence. And then you’ll group related questions into clusters.

Step 3: Convert clusters into intents and create labels

In this step, you convert the clusters into “intents.” The intents represent the user goal for different questions they may ask. For example, one user may ask, “My order hasn’t arrived — where is it?” while another might say, “I put an order into the system on Wednesday and have not received it.” With each, the user intent is to find out the status of an order. For these clusters of intents, you would then create labels. In this case, the label may simply be “Order Status.” Part of this step includes determining which labels occur frequently enough to justify the effort of turning it into an intent. Once you have a series of intents, you then edit the questions associated with the intents into simpler and unambiguous “training phrases,” which will be applied to the machine learning model.

Step 4: Write content

Now it’s time to deploy subject matter experts to craft the specific language to be used by your virtual support agent. For any customer question that is part of the “Order Status” label, your assistant might respond with, “Let me check on the status of your order,” and then ultimately go find the information and present the specific answer. That copy will need to be customized for each channel in which you’re using a virtual support agent. For example, a web-based chatbot is much different than a voice-enabled agent like Alexa. If your virtual agent is international, you’ll need to localize content for each language you plan to support.

Step 5: Load, train, and launch

Now that your customer data is structured, your AI foundation is in place and your content is written, it’s time to put it together and launch it. If you work with Directly, we have an integration team that can help you with this. Using tools provided by your VA platform, you’ll launch your VA and upload your data sets — including content — onto a staging site. You then test and train the virtual agent until it’s performing up to expectations. And at that point, it’s time to move your virtual agent to a production site and bring it to life.

Expected success rate

Now that you launched your VA, what can you expect during its “childhood”? Immature consumer AI-powered virtual agents should resolve between 30-50% of questions, while rates for more complex B2B solutions would be lower. Those cases, represented in stripes, would likely all appear in the lower section of the head of our graph, representing only the most frequently asked questions and least complex questions.

However, if the company launches and doesn’t have the resources in place to actively maintain the AI, performance won’t continue at this level. Inevitably, your virtual support agent’s effectiveness will degrade over time because the external factors related to your business and your services evolve.

A few months after launch, the curve will show regression and look something like this:

Adolescence: Maintenance and optimization of your agent

So, let’s say you launch your virtual support agent and your goal is to simply maintain the curve and your contact resolution rate. You’ll need dedicated training to ensure that the performance you see at launch stays steady. Here are the three areas you should be monitoring with your virtual agent — and the related tactics to improve in those areas:

• Intent precision: How accurate is your AI in matching the questions to the correct intents? Internal or external subject matter experts should actively review and repair the link between intents and questions. Over time, you also should be steadily growing the number of questions that are linked to each intent.
• Recall: How often is your virtual agent attempting to answer question variations that relate to existing intents? You want the AI to attempt to answer as many of the relevant questions as possible, while only escalating issues that are more complex.
• Content success: Make sure that when your virtual agent does answer questions, the content is appropriate for the intent. As part of ongoing training and maintenance, this content should be optimized over time based on customer satisfaction ratings — this is where subject matter experts also play a role — as they can evaluate and revise content as needed.

Expected success rate

Again, if the goal here is to simply maintain your AI-powered virtual agent, then your curve (below) and resolution rates should look very similar to what they do at launch. But, simply maintaining these rates means that your virtual agent may never get out of that awkward adolescent phase.

Adulthood: Pushing down the curve by advancing your AI

So, you don’t want to settle for just maintaining your virtual support agents base level performance? That’s great — because neither do we.

The good news is that improving the performance of your VA so that it reaches “adulthood” requires a similar methodology as simply maintaining it. Just a lot more of it.

With the performance attributes above — intent precision, recall and content success — companies that commit incremental increases to resources should see a correlated improvement in their contact resolution rates.

The big difference in “adulthood:” you’ll be creating many more intents and content for events that are more unusual and complex (and more likely to fall into the middle or long tail). When there are new questions emerging, you need to make sure there’s a process for capturing them and then quickly converting them into intents and creating content. For example, if new features aren’t working, that requires new intents. And as the number of intents grows, that means you’ll need to have a correlated increase in training in place to improve the intent precision, recall, and content success.

And if you want your AI-powered agent to take on more short-term issues like service outages (further down the curve), you’ll want resources and processes in place to quickly arm your virtual agent to help what we call “surge capturing.”

Expected success rate

So, if companies apply more resources to increase their resolution rate, how much can they realistically improve? That depends on the business and the resources it can dedicate. Every case is different — but you can think of the resolution rate similarly to the 80/20 rule (a.k.a. the Pareto principle). Companies can expend minimal resources to get to a base level resolution rate, but it requires increasingly more effort/resources the further down the curve you move.

With the additional committed resources, we expect that B2C company VAs can realistically achieve a 50-70% contact resolution rate and your adult virtual agent’s graph starts to look more like this:

AI Superhero: Adding the human touch your virtual agent

No virtual assistant — nor human agent, for that matter — will be able to successfully resolve 100 percent of support-related questions. Our secret sauce at Directly is the combination of AI and human experts in what we often refer to as “expert-in-the-loop” AI. The Directly CX automation platform helps our clients incentivize their networks of expert users to train the AI at all phases of virtual agent maturity. And, importantly, that expert network serves as a backstop for questions the AI can’t answer.

Our integrated platform helps companies seamlessly transition issues from an AI virtual agent to an expert, so the customer experience is painless. (Unlike the pain we’ve all encountered when we can’t find answers online — then give up and call a company’s phone support, and often remain on hold for an aggravating period of time.) So when you combine a thoroughly trained, AI-powered virtual assistant with human expertise, you’ve now achieved peak CX automation. A true AI superhero.

Expected success rate

This CX automation superhero solution can achieve contact resolution in the 80-90% range. And the first contact resolution graph is a thing of beauty, as you see near perfection in the head and significantly improved results in the medium tail and further out in the long tail.

Want to see an AI ‘superhero’ virtual agent in action? 

If you’d like to see an expert-powered AI in action, visit directly.com.

The post How to Increase Your Virtual Agent’s Customer Support Resolution Rate appeared first on Execs In The Know.